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An algorithm to generate correlated input-parameters to be used in probabilistic sensitivity analyses.

Mohamed Neine1, Desmond Curran1

  • 1GSK, Vaccines Value Evidence, Wavre, Belgium.

Journal of Market Access & Health Policy
|January 6, 2021
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Summary
This summary is machine-generated.

Incorporating parameter correlations into probabilistic sensitivity analysis (PSA) for cost-effectiveness analyses (CEAs) is crucial. This study presents an efficient algorithm that accurately models these correlations, improving uncertainty assessment in health economic evaluations.

Keywords:
Cholesky decompositionProbabilistic sensitivity analysiscorrelated parameter distributionsincremental cost-effectiveness analysis

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Area of Science:

  • Health Economics
  • Biostatistics
  • Pharmacoeconomics

Background:

  • Cost-effectiveness analyses (CEAs) require robust uncertainty assessment for informed decision-making.
  • Probabilistic sensitivity analysis (PSA) is a common method but often overlooks parameter correlations.
  • Ignoring correlations can lead to inaccurate estimations of uncertainty in CEA outcomes.

Purpose of the Study:

  • To develop and implement an efficient algorithm for integrating parameter correlations into PSA.
  • To generate multivariate non-normal parameter distributions that account for inter-parameter dependencies.
  • To evaluate the impact of parameter correlation on CEA results, specifically the incremental cost-effectiveness ratio (ICER).

Main Methods:

  • Developed a Cholesky decomposition-based algorithm to generate correlated multivariate non-normal distributions.
  • Implemented the algorithm within a herpes zoster (HZ) cost-effectiveness model.
  • Conducted 5,000 Monte Carlo simulations, varying correlation levels (0.0, 0.5, 0.9) for key parameters.

Main Results:

  • The algorithm successfully generated distributions with desired correlation coefficients for both gamma and beta distributions.
  • Increasing parameter correlation shifted the 90% confidence intervals for ICERs to higher values.
  • For instance, with 0.9 correlation, 90% of ICERs were below $38,000/QALY (incidence only) and $58,000/QALY (most parameters).

Conclusions:

  • Parameter correlations significantly influence the uncertainty surrounding CEA results.
  • The developed algorithm provides an efficient and accurate method for incorporating parameter correlation in PSA.
  • This method enhances the reliability of health economic evaluations by providing a more realistic assessment of uncertainty.